Differential binding of the three PURA HeLa iCLIPs - 3 flag vs endo
1 Input
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Dataset: endo_flag
Object of class BSFDataSet
#N Ranges: 147,893
Width ranges: 5
#N Samples: 6
#N Conditions: 2 Show code
# gene annotation
annotation <- readRDS("~/PURA/Molitor-et-al-2022/annotation.rds")
anno_txdb <- makeTxDbFromGRanges(annotation)
gns = genes(anno_txdb)
gns$gene_id = sub("\\..*", "", gns$gene_id)
idx = match(gns$gene_id, annotation$gene_id)
elementMetadata(gns) = cbind(elementMetadata(gns), elementMetadata(annotation)[idx,])
names(gns) = sub("\\..*", "", names(gns))
meta = data.frame(gene_id = gns$gene_id, gene_name = gns$gene_name, gene_type = gns$gene_type)
mcols(gns) = meta
gns$geneID = names(gns)
cdseq = cds(anno_txdb)
intrns = unlist(intronsByTranscript(anno_txdb))
utrs3 = unlist(threeUTRsByTranscript(anno_txdb))
utrs5 = unlist(fiveUTRsByTranscript(anno_txdb))
trl = GRangesList(CDS = cdseq, Intron = intrns, UTR3 = utrs3, UTR5 = utrs5)
# saveRDS(trl, paste0(outpath, "transcript_region_list_test.rds"))2 Assign genes and regions
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bds <- assignToGenes(bds, anno.genes = gns, overlaps = "frequency")
# saveRDS(bds, paste0(outpath, "bds_test.rds"))
#bds <- assignToTranscriptRegions(bds, anno.transcriptRegionList = trl, overlaps.rule = c("UTR3", "UTR5", "CDS", "Intron"))
# bds <- assignToTranscriptRegions(bds, anno.transcriptRegionList = trl, overlaps = "frequency")
# getRanges(bds)3 Calculate background
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4 Caluclate Changes
5 Changes
5.1 background changes
5.2 binding changes
Number of binding sites: 146205
Number of sig changing binding sites: 46020
Number of sig changing background: 87732
5.3 binding vs background changes
6 Save file
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# rds
saveRDS(bs, paste0(outpath, "merged_bs_diff_endo_flag_res.rds"))
# beds
up_bs <- bs |> subset((bs$bs.padj < 0.01) & (bs$bs.log2FoldChange > 0)) |>
makeGRangesFromDataFrame()
down_bs <- bs |> subset((bs$bs.padj < 0.01) & (bs$bs.log2FoldChange < 0)) |>
makeGRangesFromDataFrame()
up_bg <- bs |> subset((bs$bg.padj < 0.01) & (bs$bg.log2FoldChange > 0)) |>
makeGRangesFromDataFrame()
down_bg <- bs |> subset((bs$bg.padj < 0.01) & (bs$bg.log2FoldChange < 0)) |>
makeGRangesFromDataFrame()
rtracklayer::export.bed(up_bs, paste0("merged_bs_upbs_endovsflag.bed"))
rtracklayer::export.bed(down_bs, paste0("merged_bs_downbs_endovsflag.bed"))
rtracklayer::export.bed(up_bg, paste0("merged_bs_upbg_endovsflag.bed"))
rtracklayer::export.bed(down_bg, paste0("merged_bs_downbg_endovsflag.bed"))7 Session info
R version 4.3.1 (2023-06-16)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] DESeq2_1.41.12 SummarizedExperiment_1.31.1
[3] GenomicFeatures_1.53.3 AnnotationDbi_1.63.2
[5] Biobase_2.61.0 dplyr_1.1.3
[7] BindingSiteFinder_1.99.14 GenomicRanges_1.53.3
[9] GenomeInfoDb_1.37.7 IRanges_2.35.3
[11] S4Vectors_0.39.3 BiocGenerics_0.47.1
[13] ggplot2_3.4.4 knitr_1.44
loaded via a namespace (and not attached):
[1] RColorBrewer_1.1-3 rstudioapi_0.15.0 jsonlite_1.8.7
[4] shape_1.4.6 magrittr_2.0.3 ggbeeswarm_0.7.2
[7] farver_2.1.1 rmarkdown_2.25 GlobalOptions_0.1.2
[10] BiocIO_1.11.0 zlibbioc_1.47.0 vctrs_0.6.4
[13] Cairo_1.6-1 memoise_2.0.1 Rsamtools_2.17.0
[16] RCurl_1.98-1.12 ggpointdensity_0.1.0 rstatix_0.7.2
[19] webshot_0.5.5 htmltools_0.5.6.1 S4Arrays_1.1.6
[22] progress_1.2.2 distributional_0.3.2 curl_5.1.0
[25] broom_1.0.5 SparseArray_1.1.12 htmlwidgets_1.6.2
[28] plyr_1.8.9 cachem_1.0.8 GenomicAlignments_1.37.0
[31] lifecycle_1.0.3 iterators_1.0.14 pkgconfig_2.0.3
[34] Matrix_1.6-1.1 R6_2.5.1 fastmap_1.1.1
[37] GenomeInfoDbData_1.2.11 MatrixGenerics_1.13.2 clue_0.3-65
[40] digest_0.6.33 colorspace_2.1-0 RSQLite_2.3.1
[43] ggpubr_0.6.0 labeling_0.4.3 filelock_1.0.2
[46] fansi_1.0.5 httr_1.4.7 polyclip_1.10-6
[49] abind_1.4-5 compiler_4.3.1 bit64_4.0.5
[52] withr_2.5.1 doParallel_1.0.17 backports_1.4.1
[55] BiocParallel_1.35.4 viridis_0.6.4 carData_3.0-5
[58] DBI_1.1.3 ggforce_0.4.1 ggsignif_0.6.4
[61] biomaRt_2.57.1 MASS_7.3-60 rappdirs_0.3.3
[64] DelayedArray_0.27.10 rjson_0.2.21 tools_4.3.1
[67] vipor_0.4.5 beeswarm_0.4.0 glue_1.6.2
[70] restfulr_0.0.15 grid_4.3.1 cluster_2.1.4
[73] generics_0.1.3 gtable_0.3.4 tidyr_1.3.0
[76] hms_1.1.3 xml2_1.3.5 car_3.1-2
[79] utf8_1.2.4 XVector_0.41.2 foreach_1.5.2
[82] pillar_1.9.0 ggdist_3.3.0 stringr_1.5.0
[85] circlize_0.4.15 tweenr_2.0.2 BiocFileCache_2.9.1
[88] lattice_0.21-8 rtracklayer_1.61.2 bit_4.0.5
[91] tidyselect_1.2.0 locfit_1.5-9.8 ComplexHeatmap_2.17.0
[94] Biostrings_2.69.2 gridExtra_2.3 svglite_2.1.2
[97] xfun_0.40 matrixStats_1.0.0 stringi_1.7.12
[100] yaml_2.3.7 kableExtra_1.3.4 evaluate_0.22
[103] codetools_0.2-19 tibble_3.2.1 cli_3.6.1
[106] systemfonts_1.0.5 munsell_0.5.0 Rcpp_1.0.11
[109] dbplyr_2.3.4 png_0.1-8 ggrastr_1.0.2
[112] XML_3.99-0.14 parallel_4.3.1 blob_1.2.4
[115] prettyunits_1.2.0 bitops_1.0-7 viridisLite_0.4.2
[118] scales_1.2.1 purrr_1.0.2 crayon_1.5.2
[121] GetoptLong_1.0.5 rlang_1.1.1 KEGGREST_1.41.4
[124] rvest_1.0.3